Moradi Ali, Olanisa Olawale O, Nzeako Tochukwu, Shahrokhi Mehregan, Esfahani Eman, Fakher Nastaran, Khazeei Tabari Mohamad Amin
Internal Medicine, HCA Florida, Blake Hospital, Morsani College of Medicine, University of South Florida, Bradenton, FL 34209, USA.
Center for Translational Medicine, Semmelweis University, 1428 Budapest, Hungary.
J Imaging. 2024 Aug 8;10(8):193. doi: 10.3390/jimaging10080193.
Cardiac imaging is crucial for diagnosing heart disorders. Methods like X-rays, ultrasounds, CT scans, and MRIs provide detailed anatomical and functional heart images. AI can enhance these imaging techniques with its advanced learning capabilities.
In this scoping review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Guidelines, we searched PubMed, Scopus, Web of Science, and Google Scholar using related keywords on 16 April 2024. From 3679 articles, we first screened titles and abstracts based on the initial inclusion criteria and then screened the full texts. The authors made the final selections collaboratively.
The PRISMA chart shows that 3516 articles were initially selected for evaluation after removing duplicates. Upon reviewing titles, abstracts, and quality, 24 articles were deemed eligible for the review. The findings indicate that AI enhances image quality, speeds up imaging processes, and reduces radiation exposure with sensitivity and specificity comparable to or exceeding those of qualified radiologists or cardiologists. Further research is needed to assess AI's applicability in various types of cardiac imaging, especially in rural hospitals where access to medical doctors is limited.
AI improves image quality, reduces human errors and radiation exposure, and can predict cardiac events with acceptable sensitivity and specificity.
心脏成像对于诊断心脏疾病至关重要。X射线、超声、CT扫描和MRI等方法可提供心脏详细的解剖结构和功能图像。人工智能凭借其先进的学习能力能够增强这些成像技术。
在本综述中，我们遵循PRISMA（系统评价和Meta分析的首选报告项目）指南，于2024年4月16日使用相关关键词在PubMed、Scopus、科学网和谷歌学术上进行检索。从3679篇文章中，我们首先根据初始纳入标准筛选标题和摘要，然后筛选全文。作者共同做出最终选择。
PRISMA流程图显示，去除重复项后，最初有3516篇文章被选作评估对象。在审查标题、摘要和质量后，有24篇文章被认为符合综述要求。研究结果表明，人工智能可提高图像质量、加快成像过程并减少辐射暴露，其敏感性和特异性与合格的放射科医生或心脏病专家相当或更高。需要进一步研究以评估人工智能在各种心脏成像类型中的适用性，尤其是在医生资源有限的农村医院。
人工智能可提高图像质量、减少人为误差和辐射暴露，并能以可接受的敏感性和特异性预测心脏事件。
